An alternate approach by the Machine Learning community is to develop generic learning algorithms which can be trained to solve any combinatorial problem directly from problem instances themselves [1]}, [2]}.
Using classical problems such as TSP, Minimum Vertex Cover and Boolean Satisfiability as benchmarks, recent end-to-end approaches [3]}, [4]}, [5]} leverage advances in graph representation learning [6]}, [7]}, [8]}
and have shown competitive performance with OR solvers on trivially small problem instances up to few hundreds of nodes.
Once trained, approximate solvers based on Graph Neural Networks (GNNs)
have significantly favorable time complexity than their OR counterparts, making them highly desirable for real-time decision-making problems such as TSP and the associated class of Vehicle Routing Problems.
